Publication:
Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home

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dc.citedby0
dc.contributor.authorHusin N.S.I.M.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorJaber M.M.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorAl-Shakarchi A.H.en_US
dc.contributor.authorAbdulsattar N.F.en_US
dc.contributor.authorid58581629000en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid56519461300en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57218596226en_US
dc.contributor.authorid57866675600en_US
dc.date.accessioned2024-10-14T03:19:58Z
dc.date.available2024-10-14T03:19:58Z
dc.date.issued2023
dc.description.abstractThis paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances' energy consumption and guide them toward energy-saving practices. To achieve this, three machine learning algorithms, namely Decision Forest (DF), Boosted Decision Tree (BDT), and Linear Regression (LR), were chosen for regression tasks to estimate the energy consumption of several appliances accurately. The time-series datasets, namely appliance energy prediction datasets, are used for training and testing the algorithms. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six processing phases, was employed in this work. The test is performed by utilizing 10-fold cross-validation. The results obtained assess the models' performance in predicting the appliances' energy consumption. The experimental results indicate that the three models exhibit varying degrees of accuracy in predicting energy consumption, as measured by their respective R-squared values. Among the three models, the random forest model exhibited superior performance by achieving the highest R2 values of 0.62 and 0.54 during the training and testing phases, respectively. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/AICCIT57614.2023.10217991
dc.identifier.epage233
dc.identifier.scopus2-s2.0-85171348169
dc.identifier.spage229
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85171348169&doi=10.1109%2fAICCIT57614.2023.10217991&partnerID=40&md5=0d6a861a50a6edc60fc87b0ec36b9418
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34466
dc.pagecount4
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleAICCIT 2023 - Al-Sadiq International Conference on Communication and Information Technology
dc.subjectAppliances Energy Estimation
dc.subjectEnergy Management
dc.subjectMachine Learning
dc.subjectTime Series Dataset
dc.subjectAutomation
dc.subjectData mining
dc.subjectDecision trees
dc.subjectEnergy conservation
dc.subjectEnergy management
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMachine learning
dc.subjectRegression analysis
dc.subjectTime series
dc.subjectAppliance energy estimation
dc.subjectEnergy estimation
dc.subjectEnergy-consumption
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectSmart homes
dc.subjectThree models
dc.subjectTime series dataset
dc.subjectTimes series
dc.subjectTraining and testing
dc.subjectEnergy utilization
dc.titleMachine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Homeen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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